'''simple docstring'''





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE_:	int    =logging.getLogger()





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser()
     parser.add_argument("-f"     )
     UpperCAmelCase_			       =  parser.parse_args()
     return args.f





def       lowerCAmelCase_							(   snake_case_			:					List[Any]     )       -> str:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}
     UpperCAmelCase_			       =  os.path.join(snake_case_   ,       "all_results.json"     )
     if os.path.exists(snake_case_     ):
          with open(snake_case_   ,       "r"     ) as f:
               UpperCAmelCase_			       =  json.load(snake_case_     )
     else:
          raise ValueError(f"""can't find {path}"""     )
     return results





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  torch.cuda.is_available() and torch_device == "cuda"
     return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE_:	Any    =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			__A (					UpperCamelCase__      ):



    @classmethod
    def    _lowercase		(cls       :       Any					):
         # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
         UpperCAmelCase_			       =  tempfile.mkdtemp()
         UpperCAmelCase_			       =  os.path.join(cls.tmpdir  ,     "default_config.yml"					)
         write_basic_config(save_location=cls.configPath					)
         UpperCAmelCase_			       =  ["accelerate", "launch", "--config_file", cls.configPath]



    @classmethod
    def    _lowercase		(cls       :       int					):
         shutil.rmtree(cls.tmpdir					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         if is_cuda_and_apex_available():
              testargs.append("--fp16"					)

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.75					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "glue_no_trainer"					)					)					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         if torch.cuda.device_count() > 1:
              # Skipping because there are not enough batches to train the model + would need a drop_last to work.
              return

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertLess(result["perplexity"]  ,     100					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "clm_no_trainer"					)					)					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertLess(result["perplexity"]  ,     42					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "mlm_no_trainer"					)					)					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Optional[Any]					):
         # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
         UpperCAmelCase_			       =  7 if get_gpu_count() > 1 else 2

         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.75					)
         self.assertLess(result["train_loss"]  ,     0.5					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "ner_no_trainer"					)					)					)



    @unittest.skip(reason="Fix me @muellerzr"					)
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
         self.assertGreaterEqual(result["eval_f1"]  ,     28					)
         self.assertGreaterEqual(result["eval_exact"]  ,     28					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "qa_no_trainer"					)					)					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.8					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "swag_no_trainer"					)					)					)



    @slow
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_rouge1"]  ,     10					)
         self.assertGreaterEqual(result["eval_rouge2"]  ,     2					)
         self.assertGreaterEqual(result["eval_rougeL"]  ,     7					)
         self.assertGreaterEqual(result["eval_rougeLsum"]  ,     7					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "summarization_no_trainer"					)					)					)



    @slow
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_bleu"]  ,     30					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "translation_no_trainer"					)					)					)



    @slow
    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  logging.StreamHandler(sys.stdout					)
         logger.addHandler(__a					)

         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_overall_accuracy"]  ,     0.10					)






    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

         if is_cuda_and_apex_available():
              testargs.append("--fp16"					)

         run_command(self._launch_args + testargs					)
         UpperCAmelCase_			       =  get_results(__a					)
         # The base model scores a 25%
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.6					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "step_1"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "image_classification_no_trainer"					)					)					)









import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a      :  Optional[Any]			     =			logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None
			A      		=	None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset







	class 				_a				(   _lowerCAmelCase					):
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Union[str, Any]:
							UpperCAmelCase_:		Optional[Any]							      =     hans_processors[task]()

							UpperCAmelCase_:		Dict							      =     os.path.join(
							    SCREAMING_SNAKE_CASE_,   """cached_{}_{}_{}_{}""".format(
							        """dev""" if evaluate else """train""",   tokenizer.__class__.__name__,   str(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_,   ),   )
							UpperCAmelCase_:		Dict							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		str							      =     label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCAmelCase_:		int							      =     cached_features_file + """.lock"""
							with FileLock(SCREAMING_SNAKE_CASE_					):
										if os.path.exists(SCREAMING_SNAKE_CASE_					) and not overwrite_cache:
													logger.info(f'Loading features from cached file {cached_features_file}'					)
													UpperCAmelCase_:		Dict							      =     torch.load(SCREAMING_SNAKE_CASE_					)
										else:
													logger.info(f'Creating features from dataset file at {data_dir}'					)

													UpperCAmelCase_:		Union[str, Any]							      =     (
													    processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
													)

													logger.info("""Training examples: %s""",   len(SCREAMING_SNAKE_CASE_					)					)
													UpperCAmelCase_:		List[Any]							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
													logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
													torch.save(self.features,   SCREAMING_SNAKE_CASE_					)

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   List[str]:
							return self.label_list


if is_tf_available():
	import tensorflow as tf







	class 				_a				:
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 128,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Optional[int]:
							UpperCAmelCase_:		Union[str, Any]							      =     hans_processors[task]()
							UpperCAmelCase_:		List[Any]							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		Dict							      =     label_list

							UpperCAmelCase_:		List[str]							      =     processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
							UpperCAmelCase_:		Any							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features					),   desc="""convert examples to features"""					):
													if ex_index % 10000 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_					))					)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							UpperCAmelCase_:		List[str]							      =     tf.data.Dataset.from_generator(
							    SCREAMING_SNAKE_CASE_,   (
							        {
							            """example_id""": tf.intaa,
							            """input_ids""": tf.intaa,
							            """attention_mask""": tf.intaa,
							            """token_type_ids""": tf.intaa,
							        },
							        tf.intaa,
							    ),   (
							        {
							            """example_id""": tf.TensorShape([]					),
							            """input_ids""": tf.TensorShape([None, None]					),
							            """attention_mask""": tf.TensorShape([None, None]					),
							            """token_type_ids""": tf.TensorShape([None, None]					),
							        },
							        tf.TensorShape([]					),
							    ),   )

				def      __snake_case       (self					)       ->   Optional[Any]:
							return self.dataset

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   Union[str, Any]:
							return self.label_list







class 				_a				(   _lowerCAmelCase					):

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_train_set.txt"""					)					),   """train"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_evaluation_set.txt"""					)					),   """dev"""					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return ["contradiction", "entailment", "neutral"]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_					):
									if i == 0:
												continue
									UpperCAmelCase_:		Tuple							      =     """%s-%s""" % (set_type, line[0])
									UpperCAmelCase_:		int							      =     line[5]
									UpperCAmelCase_:		int							      =     line[6]
									UpperCAmelCase_:		List[str]							      =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
									UpperCAmelCase_:		Any							      =     line[0]
									examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_,   text_a=SCREAMING_SNAKE_CASE_,   text_b=SCREAMING_SNAKE_CASE_,   label=SCREAMING_SNAKE_CASE_,   pairID=SCREAMING_SNAKE_CASE_					)					)
						return examples







def  lowerCAmelCase_	(lowerCAmelCase__:		List[InputExample]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		PreTrainedTokenizer		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     {label: i for i, label in enumerate(lowerCAmelCase__  )}

			UpperCAmelCase_:		Optional[int]							      =     []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__  )		,						desc="""convert examples to features"""  ):
						if ex_index % 1_0_0_0_0 == 0:
									logger.info("""Writing example %d""" % (ex_index)  )

						UpperCAmelCase_:		Tuple							      =     tokenizer(
						    example.text_a		,						example.text_b		,						add_special_tokens=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						padding="""max_length"""		,						truncation=lowerCAmelCase__		,						return_overflowing_tokens=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     label_map[example.label] if example.label in label_map else 0

						UpperCAmelCase_:		Tuple							      =     int(example.pairID  )

						features.append(InputFeatures(**lowerCAmelCase__		,						label=lowerCAmelCase__		,						pairID=lowerCAmelCase__  )  )

			for i, example in enumerate(examples[:5]  ):
						logger.info("""*** Example ***"""  )
						logger.info(F'guid: {example}'  )
						logger.info(F'features: {features[i]}'  )

			return features


a      :  Optional[int]			     =			{
    'hans': 3,
}

a      :  Dict			     =			{
    'hans': HansProcessor,
}






